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This dissertation consists of two topics. Chapter 1 The Microstructure of the Reinsurance Network among US Property-Casualty Insurers and Its Effect on Insurers' Performance models the connectivity within the US property-casualty (P/C) reinsurance market as a network. It provides the first detailed empirical analysis of the microstructure of the reinsurance network including both affiliated and unaffiliated insurers. I find that reinsurance networks are highly sparse and yet largely connected, and exhibit hierarchical core-periphery structure. Moreover, an insurer's network position, measured by its network centrality, has economically significant implications for its loss experience and performance. Particularly, I find that there is an inverse U-shaped relationship between an insurer's network position and its combined ratio, and a U-shaped relationship between an insurer's network position and its performance measured by risk adjusted return on assets and risk adjusted return on equity. I also analyze the resilience of the reinsurance network against possible contagion risk by simulating economic impacts resulting from failures of one or more strategically networked reinsurers. The simulation results suggest that US Property-Casualty insurance industry is resilient to the failure of one or more top reinsurers. Chapter 2 Tail Risk Spillover and Its Contribution to Systemic Risk: A Network Analysis for Global Reinsurers analyzes the dynamic short-run tail risk dependence among global reinsurers and studies its contributions to global reinsurers' systemic risk, where a reinsurer's tail risk is measured by the Value-at-Risk. The tail risk dependence or tail risk spillover among global reinsurers is modeled as networks based on Granger Causality test. The results show that the tail risk interconnectedness among global reinsurers is subject to the impacts of both the insurance industry-wide shock and economy-wide shocks, where the former seems to have a larger effect than the latter. Moreover, I find that a reinsurer's role in the tail risk network as measured by degree/eigenvector centrality contributes significantly to its systemic risk, i.e., a more central tail risk network position will cause a higher level of systemic risk. I also find that there is a threshold effect of tail risk connectedness to systemic risk. That is, when the tail risk connectedness, as measured by daily network density, is below its median state, an increase in a reinsurer's tail risk network centrality will result in a decrease in its systemic risk possibly through risk diversification. In contrast, when the tail risk connectedness is above such threshold, an increase in the reinsurer's tail risk network centrality will lead to an increase in its systemic risk. / Business Administration/Risk Management and Insurance

Location-allocation problems arise in many practical settings and may be generically stated as follows: Given the location or distribution of a set of customers and their associated demands, simultaneously determine an optimal location of a number of supply facilities and their allocation of products or services to the customers, so as to minimize total location and transportation costs. This study is concerned with the development, convergence analysis, and testing of exact and heuristic algorithms for location-allocation problems in which demands can occur continuously over regions according to some probability density functions. In this context, minisum location problems on undirected networks are considered in which demands can occur on links with uniform probability distributions. Three types of networks are considered. The first type is a chain graph. It is shown that except for the 1-median case, the problem is generally nonconvex. However, for the p-median case, it is shown that all local and global trd.nima to the problem may be discovered by solving a series of linear programming problems. This analysis forms the basis for similar problems on trees and graphs with isolated cycles. These problems are then extended to multiperiod versions in which demands may change dynamically over time periods and at mostly one facility can be located per time period. Chain and tree graphs are considered in conjunction with three optimization strategies: myopic, long-range, and discounted present worth. It is hoped that the exact methods developed for these special networks will lead to at least effective heuristics for problems on more general networks. Finally, location-allocation problems are considered in which the region to be served is a convex polygon having a uniform demand distribution. Both single and multifacility formulations are considered. For the single facility problem, an algorithm is developed which is shown to converge to a global optimal solution. This analysis is extended to the nonconvex multifacility case, and although optimality is not guaranteed, an algorithm is presented for finding a good starting solution which increases the likelihood of finding an optimal solution. Extensions of the above problems to include discrete demand points and computational experience are also provided. / Ph. D.

Policing in America has lost more officers to suicides than line of duty deaths over the past four years. As the gatekeepers to the criminal justice system, the well-being of officers is critical as unhealthy police using poor coping strategies to handle their stress can lead to a multitude of negative consequences for the communities they serve, their departments, their fellow officers, and themselves. While the technology of policing is quickly advancing, the routine duties of officers remain stressful. This stress requires officers to use effective coping strategies to deal with it, but the traditional subculture of policing promotes maladaptive, rather than adaptive, coping strategies. To understand how the subculture influences police and the coping strategies they use, research must understand the socialization process of recruits entering the job. The current research seeks to understand how police recruits are socialized into the police subculture and how this affects the coping strategies they use to deal with the stressors they will confront on the job. The research analyzes how the network position of recruits influences their adoption of the police subculture and how this, in turn, affects their development of coping strategies. Recruits were surveyed three times during their academy training to examine the transitioning and socialization that occurs throughout the police academy. Results reveal that networks affect the adoption of the police subculture by recruits and this socialization process impacts the development of coping strategies by recruits. Findings highlight the need for future work to continue the longitudinal research approach to examine how the networks change once recruits complete their field training and probationary period. / Doctor of Philosophy / Police officers are engaged in an occupation that induces a vast amount of stress, leading to burnout and poor coping strategies. Blue H.E.L.P. began tracking the suicide rates of law enforcement and found that officers are dying more often by their own hands than in line of duty deaths. We have also seen growing tensions between police and communities, further leading to lower retention rates of current officers. The current study seeks to understand how police recruits are trained to endure the stress of their occupation. Policing is comprised of a unique occupational culture that creates solidarity among its members, which can influence how officers learn to utilize coping mechanisms. The current research examines how new police recruits fit into this occupational culture and how this affects their coping strategies over time. Results show that how new recruits are socialized into the occupational culture matter in terms of how they learn to cope with their job. Understanding how new recruits are taught to cope is imperative to destigmatize the notion of well-being to train healthier officers and to potentially lower suicide rates among our nation's law enforcement.

Rapid advances in high-throughput molecular profiling techniques enabled large-scale genomics, transcriptomics, and proteomics-based biomedical studies, generating an enormous amount of multi-omics data. Processing and summarizing multi-omics data, modeling interactions among biomolecules, and detecting condition-specific dysregulation using multi-omics data are some of the most important yet challenging analytics tasks. In the case of detecting somatic DNA copy number aberrations using bulk tumor samples in cancer research, normal cell contamination becomes one significant confounding factor that weakens the power regardless of whichever methods used for detection. To address this problem, we propose a computational approach – BACOM 2.0 to more accurately estimate normal cell fraction and accordingly reconstruct DNA copy number signals in cancer cells. Specifically, by introducing allele-specific absolute normalization, BACOM 2.0 can accurately detect deletion types and aneuploidy in cancer cells directly from DNA copy number data. Genes work through complex networks to support cellular processes. Dysregulated genes can cause structural changes in biological networks, also known as network rewiring. Genes with a large number of rewired edges are more likely to be associated with functional alteration leading phenotype transitions, and hence are potential biomarkers in diseases such as cancers. Differential dependency network (DDN) method was proposed to detect such network rewiring and biomarkers. However, the existing DDN method and software tool has two major drawbacks. Firstly, in imbalanced sample groups, DDN suffers from systematic bias and produces false positive differential dependencies. Secondly, the computational time of the block coordinate descent algorithm in DDN increases rapidly with the number of involved samples and molecular entities. To address the imbalanced sample group problem, we propose a sample-scale-wide normalized formulation to correct systematic bias and design a simulation study for testing the performance. To address high computational complexity, we propose several strategies to accelerate DDN learning, including two reformulated algorithms for block-wise coefficient updating in the DDN optimization problem. Specifically, one strategy on discarding predictors and one strategy on accelerating parallel computing. More importantly, experimental results show that new DDN learning speed with combined accelerating strategies is hundreds of times faster than that of the original method on medium-sized data. We applied the DDN method on several biomedical datasets of omics data and detected significant phenotype-specific network rewiring. With a random-graph-based detection strategy, we discovered the hub node defined biomarkers that helped to generate or validate several novel scientific hypotheses in collaborative research projects. For example, the hub genes detected by the DDN methods in proteomics data from artery samples are significantly enriched in the citric acid cycle pathway that plays a critical role in the development of atherosclerosis. To detect intra-omics and inter-omics network rewirings, we propose a method called multiDDN that uses a multi-layer signaling model to integrate multi-omics data. We adapt the block coordinate descent algorithm to solve the multiDDN optimization problem with accelerating strategies. The simulation study shows that, compared with the DDN method on single omics, the multiDDN method has considerable advantage on higher accuracy of detecting network rewiring. We applied the multiDDN method on the real multi-omics data from CPTAC ovarian cancer dataset, and detected multiple hub genes associated with histone protein deacetylation and were previously reported in independent ovarian cancer data analysis. / Doctor of Philosophy / We witnessed the start of the human genome project decades ago and stepped into the era of omics since then. Omics are comprehensive approaches for analyzing genome-wide biomolecular profiles. The rapid development of high-throughput technologies enables us to produce an enormous amount of omics data such as genomics, transcriptomics, and proteomics data, which makes researchers swim in a sea of omics information that once never imagined. Yet, the era of omics brings new challenges to us: to process the huge volumes of data, to summarize the data, to reveal the interactions between entities, to link various types of omics data, and to discover mechanisms hidden behind omics data. In processing omics data, one factor that weakens the strengths of follow up data analysis is sample impurity. We call impure tumor samples contaminated by normal cells as heterogeneous samples. The genomic signals measured from heterogeneous samples are a mixture of signals from both tumor cells and normal cells. To correct the mixed signals and get true signals from pure tumor cells, we propose a computational approach called BACOM 2.0 to estimate normal cell fraction and corrected genomics signals accordingly. By introducing a novel normalization method that identifies the neutral component in mixed signals of genomic copy number data, BACOM 2.0 could accurately detect genes' deletion types and abnormal chromosome numbers in tumor cells. In cells, genes connect to other genes and form complex biological networks to perform their functions. Dysregulated genes can cause structural change in biological networks, also known as network rewiring. In a biological network with network rewiring events, a large quantity of network rewiring linking to a single hub gene suggests concentrated gene dysregulation. This hub gene has more impact on the network and hence is more likely to associate with the functional change of the network, which ultimately leads to abnormal phenotypes such as cancer diseases. Therefore, the hub genes linked with network rewiring are potential indicators of disease status or known as biomarkers. Differential dependency network (DDN) method was proposed to detect network rewiring events and biomarkers from omics data. However, the DDN method still has a few drawbacks. Firstly, for two groups of data with unequal sample sizes, DDN consistently detects false targets of network rewiring. The permutation test, which uses the same method on randomly shuffled samples is supposed to distinguish the true targets from random effects, however, is also suffered from the same reason and could let pass those false targets. We propose a new formulation that corrects the mistakes brought by unequal group size and design a simulation study to test the new formulation's correctness. Secondly, the time used for computing in solving DDN problems is unbearably long when processing omics data with a large number of samples scale or a large number of genes. We propose several strategies to increase DDN's computation speed, including three redesigned formulas for efficiently updating the results, one rule to preselect predictor variables, and one accelerating skill of utilizing multiple CPU cores simultaneously. In the timing test, the DDN method with increased computing speed is much faster than the original method. To detect network rewirings within the same omics data or between different types of omics, we propose a method called multiDDN that uses an integrated model to process multiple types of omics data. We solve the new problem by adapting the block coordinate descending algorithm. The test on simulated data shows multiDDN is better than single omics DDN. We applied DDN or multiDDN method on several datasets of omics data and detected significant network rewiring associated with diseases. We detected hub nodes from the network rewiring events. These hub genes as potential biomarkers help us to ask new meaningful questions in related researches.

Yes / Resistance exercise training (RET) can counteract negative features of muscle ageing but older age associates with reduced adaptive capacity to RET. Altered muscle protein networks likely contribute to ageing RET adaptation; therefore, associated proteome-wide responses warrant exploration. We employed quantitative sarcoplasmic proteomics to compare age-related proteome and phosphoproteome responses to RET. Thigh muscle biopsies were collected from eight young (25 ± 1.1 years) and eight older (67.5 ± 2.6 years) adults before and after 20 weeks supervised RET. Muscle sarcoplasmic fractions were pooled for each condition and analysed using Isobaric Tags for Relative and Absolute Quantification (iTRAQ) labelling, tandem mass spectrometry and network-based hub protein identification. Older adults displayed impaired RET-induced adaptations in whole-body lean mass, body fat percentage and thigh lean mass (P > 0.05). iTRAQ identified 73 differentially expressed proteins with age and/or RET. Despite possible proteomic stochasticity, RET improved ageing profiles for mitochondrial function and glucose metabolism (top hub; PYK (pyruvate kinase)) but failed to correct altered ageing expression of cytoskeletal proteins (top hub; YWHAZ (14-3-3 protein zeta/delta)). These ageing RET proteomic profiles were generally unchanged or oppositely regulated post-RET in younger muscle. Similarly, RET corrected expression of 10 phosphoproteins altered in ageing, but these responses were again different vs. younger adults. Older muscle is characterised by RET-induced metabolic protein profiles that, whilst not present in younger muscle, improve untrained age-related proteomic deficits. Combined with impaired cytoskeletal adhesion responses, these results provide a proteomic framework for understanding and optimising ageing muscle RET adaptation. / TE was supported by a postdoctoral fellowship from the Japan Society for the Promotion of Science and the Royal Society (JSPS/FF1/435). This work was supported by grants from the Medical Research Council (MR/T026014/1 and G0801271) and the Biotechnology and Biological Sciences Research Council (BB/X510697/1 and BB/C516779/1).

Yes / The growing body of published e-government literature highlights the importance of e-government in society and the need to make sense of e-government by academia. In order to understand the future of e-government, it is important to understand the research that has been conducted and highlight the issues and themes that have been identified as important by empirical study. This paper analyses the corpus of e-government research published from 2000 to 2013 using Bibliometric and Social Network Analysis (SNA) methods to develop an intellectual structure of e-government research. Factor analysis, multidimensional scaling and centrality measurement are also applied to the e-government dataset using UCINET to identify the core influential articles in the field. This study identifies three core clusters of e-government research that centre around (i) e-government development models (ii) adoption and acceptance of e-government, and (iii) e-government using social media and highlights areas for future research in the field. Discover the world's research